Resumen

English: In the communications literature exist many documents that explain how to use spatial diversity to improve the performance of the system. However, the use of spatial diversity has not been studied in depth for GNSS, although in the last years the subject has received some interest. Lately, numerous applications of GNSS for urban indoor applications has emerged. One of the main sources of impairment in the urban and indoor environments is multipath propagation. Spatial diversity is an effective means to resolve the impact of multipath. Therefore, this Master?s Thesis addresses the problem of Time Of Arrival Estimation in DSSS based navigation systems in Non Line Of Sight Signal (NLOSS) environments using antenna array signal processing methods to mitigate the multipath and improve the quality of the signal. The proposed methods are the synchronization of the frequency and delay parameters using the Maximum Likelihood Estimator (MLE), and the use of a Minimum Mean Square Error (MMSE) spatial filtering or beamforming to remove the multipath from the input signal for a correct estimation of the frequency shift and the code delay. The thesis starts by describing the GPS signal composition and the basic theory behind the Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) based methods. The performance of the two methods is assessed through simulations and application on real measurement data. We find that ML provides the best performance while MMSE provides a better trade-off between performance and complexity.